A characterization of the extension principle
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
Fuzzy sets and applications
The principle of minimum specificity as a basis for evidential reasoning
Processing and Management of Uncertainty in Knowledge-Based Systems on Uncertainty in knowledge-based systems. International Conference on Information
Readings in nonmonotonic reasoning
Readings in nonmonotonic reasoning
On the issue of defuzzification and selection based on a fuzzy set
Fuzzy Sets and Systems
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
New Directions in Question Answering
New Directions in Question Answering
Precisiated natural language (PNL)
AI Magazine
On the retranslation process in Zadeh's paradigm of computing with words
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Conversation-Based Natural Language Interface to Relational Databases
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Comparing approximate reasoning and probabilistic reasoning using the Dempster--Shafer framework
International Journal of Approximate Reasoning
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
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We point out that question-answering systems differ from other information-seeking applications, such as search engines, by having a deduction capability, an ability to answer questions by a synthesis of information residing in different parts of its knowledge base. This capability requires appropriate representation of various types of human knowledge, rules for locally manipulating this knowledge, and a framework for providing a global plan for appropriately mobilizing the information in the knowledge to address the question posed. In this article we suggest tools to provide these capabilities. We describe how the fuzzy set–based theory of approximate reasoning can aid in the process of representing knowledge. We discuss how protoforms can be used to aid in deduction and local manipulation of knowledge. The idea of a knowledge tree is introduced to provide a global framework for mobilizing the knowledge base in response to a query. We look at some types of commonsense and default knowledge. This requires us to address the complexity of the nonmonotonicity that these types of knowledge often display. We also briefly discuss the role that Dempster-Shafer structures can play in representing knowledge. © 2006 Wiley Periodicals, Inc.